The Experts below are selected from a list of 3255 Experts worldwide ranked by ideXlab platform
Amir Poreh - One of the best experts on this subject based on the ideXlab platform.
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the rey auditory verbal learning test normative data for the arabic speaking population and analysis of the differential influence of demographic variables
Psychology and Neuroscience, 2012Co-Authors: Amir Poreh, Alya Sultan, Jennifer B LevinAbstract:The Rey Auditory Verbal Learning Test (RAVLT) is the one of the most widely used neuropsychological tests of verbal memory.It has been translated into numerous languages including Spanish, German, Arabic, Hebrew, Czech, Portuguese, and English. The present study examined the hypothesis that the learning of word lists forms an algorithmic pattern across all cultures. To this end, a sample of 200 Arabic-speaking Omani adults between the ages of 18 and 50 years was collected. The resulting norms were then compared withexisting American and Brazilian samples. The study confirmed that the first trial on the RAVLT correlates with demographic variables, whereas the learning slope on subsequent trials is almost identical across all cultures. Based on the above finding, the slope of the verbal learning test is hypothesized to measure a Psychophysiological Process linked with the hippocampal formation and allows for the laying down of new memories. In contrast, the first trial of the test is amenable to more cultural, demographic, and environmental factors.
Jennifer B Levin - One of the best experts on this subject based on the ideXlab platform.
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the rey auditory verbal learning test normative data for the arabic speaking population and analysis of the differential influence of demographic variables
Psychology and Neuroscience, 2012Co-Authors: Amir Poreh, Alya Sultan, Jennifer B LevinAbstract:The Rey Auditory Verbal Learning Test (RAVLT) is the one of the most widely used neuropsychological tests of verbal memory.It has been translated into numerous languages including Spanish, German, Arabic, Hebrew, Czech, Portuguese, and English. The present study examined the hypothesis that the learning of word lists forms an algorithmic pattern across all cultures. To this end, a sample of 200 Arabic-speaking Omani adults between the ages of 18 and 50 years was collected. The resulting norms were then compared withexisting American and Brazilian samples. The study confirmed that the first trial on the RAVLT correlates with demographic variables, whereas the learning slope on subsequent trials is almost identical across all cultures. Based on the above finding, the slope of the verbal learning test is hypothesized to measure a Psychophysiological Process linked with the hippocampal formation and allows for the laying down of new memories. In contrast, the first trial of the test is amenable to more cultural, demographic, and environmental factors.
Veljko Pejovic - One of the best experts on this subject based on the ideXlab platform.
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Cognitive Load Monitoring With Wearables–Lessons Learned From a Machine Learning Challenge
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Martin Gjoreski, Bhargavi Mahesh, Tine Kolenik, Jens Uwe-garbas, Dominik Seuss, Hristijan Gjoreski, Mitja Lustrek, Matjaz Gams, Veljko PejovicAbstract:To further extend the applicability of wearable sensors, methods for accurately extracting subtle psychological information from the sensor data are required. However, accessing subjective information in everyday life, such as cognitive load, remains challenging. To bring consensus on methods for cognitive load monitoring, a machine learning challenge is organized. The participants developed machine learning methods for cognitive load classification using wrist-worn physiological sensors’ data, namely heart rate, R-R intervals, skin conductance, and skin temperature. The data from subjects solving cognitive tasks of varying difficulty is used for the challenge. This article presents a systematic comparison and multi-strategic performance evaluation of the thirteen methods submitted to this challenge. A systematic comparison of preProcessing techniques, classification algorithms, and implementation techniques is presented. Performance variations for different task difficulty levels, different subjects, and different experiment periods are evaluated. The results indicate that the most robust methods used multimodal sensor data, classical classification approaches such as decision trees and support vector machines or their ensembles, and Bayesian hyperparameter optimization for hyperparameter tuning. The most accurate models used handcrafted features that are further selected using sequential backward floating search and evaluated using stratified person-aware cross-validation strategy. Moreover, the results indicated better classification performance for specific test subjects, the tasks with the highest difficulty, and in some cases, the time elapsed since the start of the experiment. This dependency is likely due to model overfitting or due to the subjective nature of the Psychophysiological Process. The intersubject variability in responses is challenging to be captured through objective binary labels for cognitive load, thereby warranting more sophisticated annotation approaches
Martin Gjoreski - One of the best experts on this subject based on the ideXlab platform.
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Cognitive Load Monitoring With Wearables–Lessons Learned From a Machine Learning Challenge
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Martin Gjoreski, Bhargavi Mahesh, Tine Kolenik, Jens Uwe-garbas, Dominik Seuss, Hristijan Gjoreski, Mitja Lustrek, Matjaz Gams, Veljko PejovicAbstract:To further extend the applicability of wearable sensors, methods for accurately extracting subtle psychological information from the sensor data are required. However, accessing subjective information in everyday life, such as cognitive load, remains challenging. To bring consensus on methods for cognitive load monitoring, a machine learning challenge is organized. The participants developed machine learning methods for cognitive load classification using wrist-worn physiological sensors’ data, namely heart rate, R-R intervals, skin conductance, and skin temperature. The data from subjects solving cognitive tasks of varying difficulty is used for the challenge. This article presents a systematic comparison and multi-strategic performance evaluation of the thirteen methods submitted to this challenge. A systematic comparison of preProcessing techniques, classification algorithms, and implementation techniques is presented. Performance variations for different task difficulty levels, different subjects, and different experiment periods are evaluated. The results indicate that the most robust methods used multimodal sensor data, classical classification approaches such as decision trees and support vector machines or their ensembles, and Bayesian hyperparameter optimization for hyperparameter tuning. The most accurate models used handcrafted features that are further selected using sequential backward floating search and evaluated using stratified person-aware cross-validation strategy. Moreover, the results indicated better classification performance for specific test subjects, the tasks with the highest difficulty, and in some cases, the time elapsed since the start of the experiment. This dependency is likely due to model overfitting or due to the subjective nature of the Psychophysiological Process. The intersubject variability in responses is challenging to be captured through objective binary labels for cognitive load, thereby warranting more sophisticated annotation approaches
Alya Sultan - One of the best experts on this subject based on the ideXlab platform.
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the rey auditory verbal learning test normative data for the arabic speaking population and analysis of the differential influence of demographic variables
Psychology and Neuroscience, 2012Co-Authors: Amir Poreh, Alya Sultan, Jennifer B LevinAbstract:The Rey Auditory Verbal Learning Test (RAVLT) is the one of the most widely used neuropsychological tests of verbal memory.It has been translated into numerous languages including Spanish, German, Arabic, Hebrew, Czech, Portuguese, and English. The present study examined the hypothesis that the learning of word lists forms an algorithmic pattern across all cultures. To this end, a sample of 200 Arabic-speaking Omani adults between the ages of 18 and 50 years was collected. The resulting norms were then compared withexisting American and Brazilian samples. The study confirmed that the first trial on the RAVLT correlates with demographic variables, whereas the learning slope on subsequent trials is almost identical across all cultures. Based on the above finding, the slope of the verbal learning test is hypothesized to measure a Psychophysiological Process linked with the hippocampal formation and allows for the laying down of new memories. In contrast, the first trial of the test is amenable to more cultural, demographic, and environmental factors.